Archive | Natural Language Processing

Plot of the Multichannel Convolutional Neural Network For Text

How to Develop an N-gram Multichannel Convolutional Neural Network for Sentiment Analysis

A standard deep learning model for text classification and sentiment analysis uses a word embedding layer and one-dimensional convolutional neural network. The model can be expanded by using multiple parallel convolutional neural networks that read the source document using different kernel sizes. This, in effect, creates a multichannel convolutional neural network for text that reads […]

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How to Develop a Neural Machine Translation System in Keras

How to Develop a Neural Machine Translation System from Scratch

Develop a Deep Learning Model to Automatically Translate from German to English in Python with Keras, Step-by-Step. Machine translation is a challenging task that traditionally involves large statistical models developed using highly sophisticated linguistic knowledge. Neural machine translation is the use of deep neural networks for the problem of machine translation. In this tutorial, you […]

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How to Prepare a French-to-English Dataset for Machine Translation

How to Prepare a French-to-English Dataset for Machine Translation

Machine translation is the challenging task of converting text from a source language into coherent and matching text in a target language. Neural machine translation systems such as encoder-decoder recurrent neural networks are achieving state-of-the-art results for machine translation with a single end-to-end system trained directly on source and target language. Standard datasets are required […]

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How to Implement Beam Search Decoder for Natural Language Processing

How to Implement a Beam Search Decoder for Natural Language Processing

Natural language processing tasks, such as caption generation and machine translation, involve generating sequences of words. Models developed for these problems often operate by generating probability distributions across the vocabulary of output words and it is up to decoding algorithms to sample the probability distributions to generate the most likely sequences of words. In this […]

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How to Configure an Encoder-Decoder Model for Neural Machine Translation

How to Configure an Encoder-Decoder Model for Neural Machine Translation

The encoder-decoder architecture for recurrent neural networks is achieving state-of-the-art results on standard machine translation benchmarks and is being used in the heart of industrial translation services. The model is simple, but given the large amount of data required to train it, tuning the myriad of design decisions in the model in order get top […]

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Encoder-Decoder Recurrent Neural Network Models for Neural Machine Translation

Encoder-Decoder Recurrent Neural Network Models for Neural Machine Translation

The encoder-decoder architecture for recurrent neural networks is the standard neural machine translation method that rivals and in some cases outperforms classical statistical machine translation methods. This architecture is very new, having only been pioneered in 2014, although, has been adopted as the core technology inside Google’s translate service. In this post, you will discover […]

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A Gentle Introduction to Neural Machine Translation

A Gentle Introduction to Neural Machine Translation

One of the earliest goals for computers was the automatic translation of text from one language to another. Automatic or machine translation is perhaps one of the most challenging artificial intelligence tasks given the fluidity of human language. Classically, rule-based systems were used for this task, which were replaced in the 1990s with statistical methods. […]

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Caption Generation with the Inject and Merge Architectures for the Encoder-Decoder Model

Caption Generation with the Inject and Merge Architectures for the Encoder-Decoder Model

Caption generation is a challenging artificial intelligence problem that draws on both computer vision and natural language processing. The encoder-decoder recurrent neural network architecture has been shown to be effective at this problem. The implementation of this architecture can be distilled into inject and merge based models, and both make different assumptions about the role […]

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Encoder-Decoder Models for Text Summarization in Keras

Encoder-Decoder Models for Text Summarization in Keras

Text summarization is a problem in natural language processing of creating a short, accurate, and fluent summary of a source document. The Encoder-Decoder recurrent neural network architecture developed for machine translation has proven effective when applied to the problem of text summarization. It can be difficult to apply this architecture in the Keras deep learning […]

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